responsibility-framing/predict-perception-bert-cause-none
013
1---2license: mit3tags:4- generated_from_trainer5model-index:6- name: predict-perception-bert-cause-none7 results: []8---9 10<!-- This model card has been generated automatically according to the information the Trainer had access to. You11should probably proofread and complete it, then remove this comment. -->12 13# predict-perception-bert-cause-none14 15This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on an unknown dataset.16It achieves the following results on the evaluation set:17- Loss: 1.626918- Rmse: 1.276319- Rmse Cause::a Spontanea, priva di un agente scatenante: 1.276320- Mae: 1.043121- Mae Cause::a Spontanea, priva di un agente scatenante: 1.043122- R2: -1.432923- R2 Cause::a Spontanea, priva di un agente scatenante: -1.432924- Cos: -0.391325- Pair: 0.026- Rank: 0.527- Neighbors: 0.337128- Rsa: nan29 30## Model description31 32More information needed33 34## Intended uses & limitations35 36More information needed37 38## Training and evaluation data39 40More information needed41 42## Training procedure43 44### Training hyperparameters45 46The following hyperparameters were used during training:47- learning_rate: 1e-0548- train_batch_size: 2049- eval_batch_size: 850- seed: 199651- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0852- lr_scheduler_type: linear53- num_epochs: 3054 55### Training results56 57| Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Cause::a Spontanea, priva di un agente scatenante | Mae | Mae Cause::a Spontanea, priva di un agente scatenante | R2 | R2 Cause::a Spontanea, priva di un agente scatenante | Cos | Pair | Rank | Neighbors | Rsa |58|:-------------:|:-----:|:----:|:---------------:|:------:|:------------------------------------------------------:|:------:|:-----------------------------------------------------:|:-------:|:----------------------------------------------------:|:-------:|:----:|:----:|:---------:|:---:|59| 0.994 | 1.0 | 15 | 0.7156 | 0.8465 | 0.8465 | 0.7809 | 0.7809 | -0.0701 | -0.0701 | -0.1304 | 0.0 | 0.5 | 0.2971 | nan |60| 0.9757 | 2.0 | 30 | 0.7096 | 0.8429 | 0.8429 | 0.7666 | 0.7666 | -0.0611 | -0.0611 | 0.0435 | 0.0 | 0.5 | 0.2515 | nan |61| 1.0086 | 3.0 | 45 | 0.7779 | 0.8825 | 0.8825 | 0.7981 | 0.7981 | -0.1632 | -0.1632 | -0.0435 | 0.0 | 0.5 | 0.2899 | nan |62| 0.9127 | 4.0 | 60 | 0.8158 | 0.9038 | 0.9038 | 0.8171 | 0.8171 | -0.2199 | -0.2199 | -0.2174 | 0.0 | 0.5 | 0.2975 | nan |63| 0.8555 | 5.0 | 75 | 0.7691 | 0.8775 | 0.8775 | 0.8121 | 0.8121 | -0.1501 | -0.1501 | -0.2174 | 0.0 | 0.5 | 0.3299 | nan |64| 0.8702 | 6.0 | 90 | 0.7818 | 0.8848 | 0.8848 | 0.7781 | 0.7781 | -0.1691 | -0.1691 | 0.0435 | 0.0 | 0.5 | 0.2515 | nan |65| 0.76 | 7.0 | 105 | 0.8377 | 0.9158 | 0.9158 | 0.7985 | 0.7985 | -0.2526 | -0.2526 | 0.0435 | 0.0 | 0.5 | 0.2515 | nan |66| 0.6997 | 8.0 | 120 | 0.9065 | 0.9527 | 0.9527 | 0.8370 | 0.8370 | -0.3555 | -0.3555 | -0.2174 | 0.0 | 0.5 | 0.3147 | nan |67| 0.5963 | 9.0 | 135 | 1.0611 | 1.0308 | 1.0308 | 0.8396 | 0.8396 | -0.5867 | -0.5867 | -0.0435 | 0.0 | 0.5 | 0.2645 | nan |68| 0.5413 | 10.0 | 150 | 1.1724 | 1.0835 | 1.0835 | 0.8649 | 0.8649 | -0.7532 | -0.7532 | -0.0435 | 0.0 | 0.5 | 0.2645 | nan |69| 0.4994 | 11.0 | 165 | 1.1471 | 1.0717 | 1.0717 | 0.8857 | 0.8857 | -0.7154 | -0.7154 | -0.2174 | 0.0 | 0.5 | 0.3271 | nan |70| 0.4208 | 12.0 | 180 | 1.2136 | 1.1024 | 1.1024 | 0.9392 | 0.9392 | -0.8148 | -0.8148 | -0.2174 | 0.0 | 0.5 | 0.3169 | nan |71| 0.316 | 13.0 | 195 | 1.3499 | 1.1626 | 1.1626 | 0.9395 | 0.9395 | -1.0187 | -1.0187 | -0.2174 | 0.0 | 0.5 | 0.3271 | nan |72| 0.2893 | 14.0 | 210 | 1.4229 | 1.1937 | 1.1937 | 0.9608 | 0.9608 | -1.1278 | -1.1278 | -0.3043 | 0.0 | 0.5 | 0.3269 | nan |73| 0.235 | 15.0 | 225 | 1.4699 | 1.2132 | 1.2132 | 0.9785 | 0.9785 | -1.1981 | -1.1981 | -0.0435 | 0.0 | 0.5 | 0.2865 | nan |74| 0.2397 | 16.0 | 240 | 1.5492 | 1.2455 | 1.2455 | 1.0005 | 1.0005 | -1.3167 | -1.3167 | -0.0435 | 0.0 | 0.5 | 0.2655 | nan |75| 0.1973 | 17.0 | 255 | 1.5541 | 1.2474 | 1.2474 | 1.0165 | 1.0165 | -1.3239 | -1.3239 | -0.0435 | 0.0 | 0.5 | 0.2655 | nan |76| 0.1793 | 18.0 | 270 | 1.4966 | 1.2242 | 1.2242 | 1.0058 | 1.0058 | -1.2380 | -1.2380 | -0.3043 | 0.0 | 0.5 | 0.3437 | nan |77| 0.16 | 19.0 | 285 | 1.4977 | 1.2246 | 1.2246 | 1.0140 | 1.0140 | -1.2396 | -1.2396 | -0.3913 | 0.0 | 0.5 | 0.3371 | nan |78| 0.1501 | 20.0 | 300 | 1.5751 | 1.2558 | 1.2558 | 1.0254 | 1.0254 | -1.3553 | -1.3553 | -0.3913 | 0.0 | 0.5 | 0.3371 | nan |79| 0.1342 | 21.0 | 315 | 1.7011 | 1.3051 | 1.3051 | 1.0681 | 1.0681 | -1.5438 | -1.5438 | -0.2174 | 0.0 | 0.5 | 0.2715 | nan |80| 0.137 | 22.0 | 330 | 1.5557 | 1.2481 | 1.2481 | 1.0393 | 1.0393 | -1.3263 | -1.3263 | -0.3043 | 0.0 | 0.5 | 0.3437 | nan |81| 0.11 | 23.0 | 345 | 1.5475 | 1.2448 | 1.2448 | 1.0320 | 1.0320 | -1.3141 | -1.3141 | -0.3913 | 0.0 | 0.5 | 0.3371 | nan |82| 0.1106 | 24.0 | 360 | 1.6006 | 1.2660 | 1.2660 | 1.0452 | 1.0452 | -1.3936 | -1.3936 | -0.3913 | 0.0 | 0.5 | 0.3297 | nan |83| 0.1013 | 25.0 | 375 | 1.5907 | 1.2621 | 1.2621 | 1.0368 | 1.0368 | -1.3787 | -1.3787 | -0.3043 | 0.0 | 0.5 | 0.2929 | nan |84| 0.0863 | 26.0 | 390 | 1.6436 | 1.2829 | 1.2829 | 1.0496 | 1.0496 | -1.4578 | -1.4578 | -0.3043 | 0.0 | 0.5 | 0.2929 | nan |85| 0.0929 | 27.0 | 405 | 1.6000 | 1.2658 | 1.2658 | 1.0341 | 1.0341 | -1.3927 | -1.3927 | -0.3043 | 0.0 | 0.5 | 0.3245 | nan |86| 0.0829 | 28.0 | 420 | 1.6277 | 1.2767 | 1.2767 | 1.0422 | 1.0422 | -1.4341 | -1.4341 | -0.3913 | 0.0 | 0.5 | 0.3371 | nan |87| 0.0884 | 29.0 | 435 | 1.6324 | 1.2785 | 1.2785 | 1.0436 | 1.0436 | -1.4411 | -1.4411 | -0.3913 | 0.0 | 0.5 | 0.3371 | nan |88| 0.0896 | 30.0 | 450 | 1.6269 | 1.2763 | 1.2763 | 1.0431 | 1.0431 | -1.4329 | -1.4329 | -0.3913 | 0.0 | 0.5 | 0.3371 | nan |89 90 91### Framework versions92 93- Transformers 4.16.294- Pytorch 1.10.2+cu11395- Datasets 1.18.396- Tokenizers 0.11.097